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改进丰富卷积特征算法的液滴边缘检测模型

王慧 曹召良 王军

王慧, 曹召良, 王军. 改进丰富卷积特征算法的液滴边缘检测模型[J]. 中国光学(中英文). doi: 10.37188/CO.2024-0019
引用本文: 王慧, 曹召良, 王军. 改进丰富卷积特征算法的液滴边缘检测模型[J]. 中国光学(中英文). doi: 10.37188/CO.2024-0019
WANG Hui, CAO Zhao-liang, WANG Jun. Improved droplet edge detection model based on RCF algorithm[J]. Chinese Optics. doi: 10.37188/CO.2024-0019
Citation: WANG Hui, CAO Zhao-liang, WANG Jun. Improved droplet edge detection model based on RCF algorithm[J]. Chinese Optics. doi: 10.37188/CO.2024-0019

改进丰富卷积特征算法的液滴边缘检测模型

doi: 10.37188/CO.2024-0019
基金项目: “十四五”江苏省重点学科资助(No. 2021135);中国航天科技集团公司第八研究院产学研合作基金资助(No. SAST2020-025)
详细信息
    作者简介:

    王 慧(1997—),女,山东泰安人,硕士研究生,学生,主要从事图像处理、光电仪器与智能检测技术的研究。E-mail:2352115270@qq.com

    王 军(1979—),男,江苏睢宁人,博士,副教授,2005年于中国科学长春光学精密机械与物理研究所获得博士学位,主要从事光电测控技术、图像信息处理技术以及物联网工程应用方面的研究。E-mail:wjyhl@126.com

  • 中图分类号: TP394.4;TH691.9

Improved droplet edge detection model based on RCF algorithm

Funds: Supported by Jiangsu Key Disciplines of the Fourteenth Five-Year Plan (No. 2021135); Industry-University-Institute Cooperation Foundation of the Eighth Research Institute of China Aerospace Science and Technology Corporation (No. SAST2020-025)
More Information
  • 摘要:

    液滴图像边缘的高精度提取是测量水接触角较为关键的一环,针对常规边缘提取方法噪声鲁棒性差、边缘提取不完整、精度低的问题,本文提出了一种改进RCF的液滴边缘检测模型。首先,在深度特征提取阶段引入特征融合模块,使用多个特征让模型更加鲁棒,减少过拟合的风险;其次,设计多感受野模块代替RCF后边的contact层,通过多个感受野来提取更多的语义信息,使边缘细节更加丰富;然后,在模型每一层之前引入高效通道注意力机制,增强模型对图像中重要特征的关注程度;最后,设计并引入MaxBlurPool下采样技术,减少计算量和参数量,提高平移不变性。在自制液滴数据集上的实验结果表明,本文模型的固定轮廓阈值(ODS)值提高到0.816、单图像最佳阈值(OIS)提高到0.829、检测准确率高达90.17%,相较原模型提高了1.85个百分点,能够准确检测液滴边缘特征。

     

  • 图 1  RCF模型结构

    Figure 1.  RCF model structure

    图 2  改进的RCF模型结构

    Figure 2.  Improved RCF model structure

    图 3  特征融合

    Figure 3.  Feature fusion

    图 4  多感受野模块

    Figure 4.  Multi-receptive field module

    图 5  高效注意力机制模块

    Figure 5.  Efficient attention mechanism module

    图 6  抗锯齿的最大池化

    Figure 6.  Anti-aliasing for maximum pooling

    图 7  一维抗锯齿操作

    Figure 7.  One-dimensional anti-aliasing operation

    图 8  数据样本

    Figure 8.  Data samples

    图 9  传统RCF损失值与迭代次数的关系

    Figure 9.  The relationship between RCF loss and the number of iterations

    图 10  改进RCF损失值与迭代次数的关系

    Figure 10.  The relationship between loss of improved RCF algorithm and the number of iterations

    图 11  较小角度液滴边缘检测结果

    Figure 11.  Edge detection results for small-angle liquid droplet

    图 12  较大角度液滴边缘检测结果

    Figure 12.  Edge detection results for large-angle liquid droplet

    表  1  改进模型各阶段性能

    Table  1.   Performance of the improved model at each stage

    Each stagePrecision/%Recall/%F-measure/%
    Stage172.2673.3872.82
    Stage278.3181.6979.96
    Stage385.6587.4186.52
    Stage488.9289.1589.03
    Mixed Output90.1789.6489.90
    下载: 导出CSV

    表  2  本文模型与其他算法结果比较

    Table  2.   Results comparison of proposed model and the relevant algorithms

    Algorithm Precision/% ODS OIS OR/% Time/s
    Canny 81.02 0.717 0.642 11.25 0.015
    HED 87.49 0.732 0.735 7.36 0.025
    RCF 88.32 0.783 0.792 6.74 0.028
    Improved-RCF 90.17 0.816 0.829 5.10 0.041
    下载: 导出CSV

    表  3  消融实验结果对比

    Table  3.   Comparison of ablation experimental results

    ModuleODSOIS
    BasicRCF0.7830.792
    BasicRCF+FM0.7950.812
    BasicRCF+FM+DC0.8090.817
    BasicRCF+FM+DC+EN0.8110.823
    BasicRCF+FM+DC+EN+MP0.8160.829
    下载: 导出CSV
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  • 收稿日期:  2024-01-18
  • 录用日期:  2024-03-15
  • 网络出版日期:  2024-05-10

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